IEEE Transactions on Biomedical Engineering最新文献

Variable-length time series classification (VTSC) problems are prevalent in healthcare applications, such as heart rate monitoring and electrophysiological recordings, where sequence length varies among patients and events. VTSC is challenging as finite-context models such as Transformers require padding, truncation, or interpolation, leading to distortion in the input data, higher computational costs, and overfitting, while infinite-context models including recurrent neural networks struggle with overcompression and unstable gradients over long sequences. In this paper, we develop a novel VTSC framework based on Stochastic Sparse Sampling (SSS) for seizure onset zone (SOZ) localization, a critical VTSC problem requiring identification of seizure-inducing brain regions from variable-length electrophysiological time series. The proposed framework sparsely samples time series windows to compute local predictions, which are then aggregated and calibrated to form a global prediction. SSS provides post-hoc insights into local signal characteristics related to the SOZ, by visualizing temporally averaged local predictions throughout the signal. We evaluate our method on the Epilepsy intracranial electroencephalography (iEEG) Multicenter Dataset, a heterogeneous collection of iEEG recordings obtained from four independent medical centers. The proposed solution outperforms state-of-the-art (SOTA) baselines across most medical centers, and superior performance on all out-of-distribution (OOD) unseen medical centers.

Identifying homogeneous subgroups with similar symptoms or neuropsychological patterns is essential for understanding the heterogeneity of psychotic disorders and advancing precision medicine, which enables tailored treatments based on patients' unique profiles. Existing data-driven methods, such as independent component analysis or independent vector analysis (ICA/IVA) applied to multi-subject functional magnetic resonance imaging (fMRI) data, have successfully revealed meaningful subgroups. However, these methods often rely on single-dimensional information, such as isolated functional networks, or assume uniform subgroup structures across all networks. Given the complexity of psychiatric disorders, exploring relationships across multiple functional networks can provide deeper insights into diagnostic heterogeneity. To address this, we propose a novel method that integrates cross-functional network information for subgroup identification by constructing multiplex networks from functional connectivity networks extracted from multi-subject resting-state fMRI data. Multiplex network-based community detection is then applied to identify both common communities spanning multiple networks and private communities specific to individual networks. Results from simulations and real-world fMRI data demonstrate the effectiveness of the proposed method. In a study of 464 psychotic patients, the identified subgroups exhibit significant differences in key functional areas, such as the default mode network (DMN) and anterior prefrontal cortex (antPFC), as well as corresponding clinical scores. These findings align with prior clinical studies, demonstrating the ability of the proposed approach to uncover clinically relevant subgroups and enhance understanding of psychotic disorder heterogeneity. By considering multi-dimensional information across functional networks, this approach provides a framework for understanding individual variability in psychotic disorders and paves the way for precision medicine.

Estimating the instantaneous phase of neural oscillations is crucial for technology that interfaces with the brain, such as brain-computer interfaces (BCIs) and neuromodulation systems. In these systems, phase information from the oscillating neural signal can be used to guide subsequent decisions to apply experimental perturbation. Traditional methods for phase estimation rely on the Hilbert transform computed using the Discrete Fourier Transform (DFT), which introduces a phase lag due to dependency on past and present signal values. This paper proposes a deep learning algorithm utilizing a dual-branch structure similar to the complex wavelet transform to generate a pseudo-complex valued signal for instantaneous phase estimation. The network has Discrete Cosine Transform (DCT) layers, which help to extract latent space representations for the real and imaginary signal components, respectively. An additional design goal was to make this Deep Learning (DL)-based algorithm suitable for deployment on portable edge devices with limited computing resources such as field-programmable gate arrays (FPGAs). This work demonstrates a proof-of-principle for real-time instantaneous phase estimation in neuromodulation applications. Our generalized model achieves an improvement of 40.3% in phase estimation accuracy over the endpoint-corrected Hilbert Transform (ecHT) method and an improvement of 9.2% over conventional deep learning model architectures.

Speaker identification in auditory attention decoding (SI-AAD) aims to identify the attended speaker from electroencephalography (EEG) signals. However, its application for hearing-impaired individuals is limited since existing methods rarely consider altered auditory perception from hearing-assistive devices under mixed-speech conditions, compounded by the lack of relevant datasets and difficulties in learning robust EEG-speech correspondences due to weak cross-modal alignment and insufficient feature extraction. Therefore, we construct five mixed-speech AAD datasets (MS-AAD), serving as the first EEG benchmark to simulate typical device-induced acoustic alterations without spatial cues. To enhance modality alignment, we propose a timbre-enhanced latent alignment (TELA) framework that jointly models latent embeddings and perceptual speaker cues via contrastive learning and auxiliary timbre classification. To further improve EEG-based feature extraction, we design FCTNet, a frequency-channel-temporal attention-based EEG encoder that captures rich neural patterns across multiple domains. Experiments on MS-AAD demonstrate that TELA and FCTNet jointly achieve 89.5% SI-AAD accuracy across diverse hearing conditions, highlighting the critical role of device-simulated acoustic dataset design and perceptually guided representation learning with advanced EEG encoding in mixed-speech SI-AAD for hearing-assistive applications.

Objective: Recent outbreaks and pandemics have emphasized the need for safe and reliable viral inactivation methods. The purpose of this work is to develop a simple and effective modeling approach to investigate viral inactivation via microwave absorption mediated by dipolar coupling.

Methods: Leveraging established techniques from the dynamic analysis of structures, a generalized Single-Degree-Of-Freedom (SDOF) model is developed, which is fully consistent with the dipolar resonance mode.

Results: The model can reproduce the main features of dipolar coupling with minimal computational time. Moreover, it allows mimicking the broadening of the resonance range associated with heterogeneous virus size, via Monte Carlo simulations, as well as water induced damping.

Conclusion: The results support the potential role of dipolar coupling for viral inactivation by microwave irradiation in the GHz range. The model can be used to assist in the interpretation of the experimental results, leading to an optimization of the inactivation protocols.

Significance: The proposed approach is versatile and can be extended to describe more complex cases, such as non-spherical geometries and/or non-homogeneous material properties.

Objective: Recent neuroscientific research craves for understanding sophisticated brain networks formed by neuron ensembles across multiple regions. An ideal way to unveil the complex connectome is bidirectionally interacting (simultaneous recording and stimulation) with neurons at high spatiotemporal resolutions. Existing CMOS recording probes cannot provide micro-scale interactions with limited stimulation capability. Although optogenetics can achieve neuron-specific stimulation, conventional methods using optic fibers illuminate a large volume of tissue, resulting in unspecific perturbations. While our previous studies demonstrated micro-LED (μLED)-based optoelectrode for localized stimulation and recording, this work advances them into a fully integrated headstage combining the optoelectrode, CMOS IC, and flexible interposer for miniaturized implementation. The proposed system enables micro-scale interactions with high spatiotemporal precision through densely packed 256-neuron-size recording and 128-soma-size fiber-less opto-stimulation across multiple brain regions.

Methods: Such high resolutions yet wide coverage is achieved by (1) advanced micromachining techniques integrating recording electrodes and μLEDs, (2) micro-second, independent 384-channel interaction via a low-power, area-efficient circuit, and (3) compact and reliable polyimide-cable-based hybrid assembly.

Results: A compact (23.8×28.8 mm2) and lightweight (3.5-gram) headstage achieved the highest reported channel density in area (0.56 channels/mm2) and weight (109.71 channels/gram). A single acute in vivo experiment on a transgenic mouse identified >160 isolated pyramidal neurons and narrow/wide interneurons in the dorsal hippocampus, with local and broad-range effects from focal optogenetic stimulation.

Conclusion and significance: We implemented the hybrid integrated, large-scale opto-electrophysiology interface prototype and verified its feasibility in vivo, representing the first fully integrated platform extending our μLED-based probes into a complete system.

Traditionally, venous stents have been employed to maintain vessel patency in cases of venous obstruction. Recent advancements in stent-electrode technology have broadened their application to include endovascular neural interfaces for neurotechnological purposes within cerebral veins. However, the effects of neointimal hyperplasia on large venous sinuses, particularly the superior sagittal sinus and jugular vein, remain poorly understood. Additionally, concerns such as thrombosis, chronic inflammation, and tissue overgrowth pose challenges for their long-term use as neural interfaces.

Methods: To investigate the impact of venous stenting on blood flow and tissue growth, we utilized Computational Fluid Dynamics (CFD) modeling and animal experiments, assessing blood flow and tissue responses over 28 days.

Results: Our findings revealed a negative power law correlation, with low wall shear stress (WSS) identified as the primary driver of accelerated tissue growth. Unlike the focal narrowing typically observed in stented arteries, venous tissue growth exhibited greater variability. Additionally, the threshold for low WSS that triggered growth was smaller than previously reported in arteries.

Conclusion: This study provides new insights into venous neointimal hyperplasia, emphasizing the need to consider venous-specific responses in stent-electrode design and clinical applications. Nonetheless, potential risks such as thrombosis and inflammatory responses should be further investigated to fully understand the long-term viability of these devices.

Significance: Understanding the biomechanical environment of stents in cerebral veins can guide the development of next-generation neural interfaces and inform clinicians and device developers about potential impacts on long-term outcomes.

Objective: Label-free electrical impedance-based single-cell detection has been widely applied in cell sorting, electrical phenotyping, and monitoring of cell growth status. However, when high-concentration cell suspensions pass through the sensing region simultaneously, coincident events frequently occur, which leads to inaccurate segmentation of cell events and distorted identification of single-cell waveforms. As a result, statistical errors in electrical phenotyping are introduced.

Methods: In this work, we propose a two-step sparse-constrained optimization algorithm based on $ell _{1}$-norm regularization, which addresses this challenge without requiring any structural modification to the microfluidic chip. The raw signal is processed using this two-step framework: first, a waveform detection dictionary is constructed to segment the signal; subsequently, a de-coincidence dictionary is applied to resolve coincident waveforms.

Results: Experimental validation on synthetic data streams demonstrates robust counting accuracy from 2×10⁵ to 5×10⁶ particles/ml (99.9%-98.4%), with only a 5.1% reduction under five levels of additive noise at 2×10⁶ particles/ml. Analysis of polystyrene beads of two sizes and T cells at three concentrations demonstrates enhanced size discrimination, improved statistical accuracy, and consistent counting performance compared with conventional algorithms.

Conclusion: The proposed method effectively segments and decomposes coincident signals into individual cell events by employing sparse optimization techniques.

Significance: This algorithm is well suited for applications that demand accurate counting and classification of cell/particle suspensions across a wide concentration range.

Objective: In this work, we propose a framework for differentiable forward and back-projector that enables scalable, accurate, and memory-efficient gradient computation for rigid motion estimation tasks.

Methods: Unlike existing approaches that rely on auto-differentiation or that are restricted to specific projector types, our method is based on a general analytical gradient formulation for forward/backprojection in the continuous domain. A key insight is that the gradients of both forward and back-projection can be expressed directly in terms of the forward and back-projection operations themselves, providing a unified gradient computation scheme across different projector types. Leveraging this analytical formulation, we develop a discretized implementation with an acceleration strategy that balances computational speed and memory usage.

Results: Simulation studies illustrate the numerical accuracy and computational efficiency of the proposed algorithm. Experiments demonstrates the effectiveness of this approach for multiple X-ray imaging tasks we conducted. In 2D/3D registration, the proposed method achieves $sim$8× speedup over an existing differentiable forward projector while maintaining comparable accuracy. In motion-compensated analytical reconstruction and cone-beam CT geometry calibration, the proposed method enhances image sharpness and structural fidelity on real phantom data while showing significant efficiency advantages over existing gradient-free and gradient-based solutions.

Conclusion: The proposed differentiable projectors enable effective and efficient gradient-based solutions for X-ray imaging tasks requiring rigid motion estimation.

Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we propose a structured and sparse partial least squares coherence algorithm (ssPLSC) to extract shared latent space representations related to cortico-muscular interactions. Our approach leverages an embedded optimization framework by integrating a partial least square (PLS)-based objective function with sparsity and connectivity-based structured constraints, addressing the generalizability, sparsity and spatial structure. To solve the optimization problem, we develop an efficient alternating iterative algorithm within a unified framework and prove its convergence experimentally. Extensive experimental results from one synthetic and several real-world datasets have demonstrated that ssPLSC can achieve competitive or better performance over some representative multivariate cortico-muscular fusion methods, particularly in scenarios characterized by limited sample sizes and high noise levels. This study provides a novel multivariate fusion method for cortico-muscular analysis, offering a potential tool for the evaluation of corticospinal pathway integrity in neurological disorders.